from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

Daal4py_kmeans_short: 0h 0m 1s

Daal4py_ridge: 0h 0m 1s

Kmeans_short: 0h 0m 2s

Daal4py_logisticregression: 0h 0m 3s

Daal4py_kmeans_tall: 0h 0m 7s

Ridge: 0h 0m 10s

Logisticregression: 0h 0m 18s

Kmeans_tall: 0h 0m 21s

Daal4py_kneighborsclassifier_kd_tree: 0h 0m 25s

Daal4py_kneighborsclassifier: 0h 2m 27s

Kneighborsclassifier_kd_tree: 0h 2m 27s

Histgradientboostingclassifier: 0h 5m 1s

Catboost_symmetric: 0h 5m 2s

Catboost: 0h 5m 9s

Lightgbm: 0h 5m 10s

Xgboost: 0h 5m 13s

Kneighborsclassifier: 0h 38m 42s

Total: 1h 10m 49s

print_env_info()

Benchmark environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.128	0.000	6.244	0.000	1	100	NaN	NaN	0.451	0.000	0.284	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	17.404	0.112	0.000	0.017	1	100	0.932	0.713	1.718	0.011	10.130	0.091	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.001	0.000	0.183	1	100	1.000	0.000	0.078	0.000	2.333	0.015	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.588	0.000	1	1	NaN	NaN	0.439	0.000	0.276	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.146	0.109	0.000	0.012	1	1	0.727	0.713	1.800	0.042	6.746	0.167	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.173	0.004	0.000	0.173	1	1	1.000	0.000	0.080	0.001	2.163	0.049	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.122	0.000	6.568	0.000	-1	1	NaN	NaN	0.442	0.000	0.275	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.780	0.140	0.000	0.023	-1	1	0.727	0.819	1.763	0.035	12.925	0.265	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.148	0.013	0.000	0.148	-1	1	1.000	1.000	0.077	0.000	1.911	0.164	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.120	0.000	6.658	0.000	1	5	NaN	NaN	0.437	0.000	0.275	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	17.369	0.134	0.000	0.017	1	5	0.821	0.949	1.773	0.014	9.798	0.109	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.179	0.001	0.000	0.179	1	5	1.000	1.000	0.077	0.000	2.330	0.015	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.115	0.000	6.957	0.000	-1	100	NaN	NaN	0.439	0.000	0.262	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.992	0.259	0.000	0.028	-1	100	0.932	0.949	1.863	0.018	15.022	0.204	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.162	0.011	0.000	0.162	-1	100	1.000	1.000	0.080	0.001	2.016	0.138	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.118	0.000	6.780	0.000	-1	5	NaN	NaN	0.444	0.000	0.265	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.989	0.335	0.000	0.028	-1	5	0.821	0.819	1.737	0.031	16.118	0.350	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.168	0.010	0.000	0.168	-1	5	1.000	1.000	0.081	0.007	2.087	0.231	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.297	0.000	1	100	NaN	NaN	0.097	0.000	0.554	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.504	0.092	0.000	0.020	1	100	0.990	0.974	0.264	0.002	73.847	0.644	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.001	0.000	0.023	1	100	1.000	1.000	0.005	0.000	4.400	0.211	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.051	0.000	0.311	0.000	1	1	NaN	NaN	0.098	0.000	0.527	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.447	0.052	0.000	0.009	1	1	0.979	0.974	0.262	0.002	36.024	0.393	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.013	0.000	0.000	0.013	1	1	1.000	1.000	0.005	0.000	2.460	0.152	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.308	0.000	-1	1	NaN	NaN	0.097	0.000	0.534	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.238	0.191	0.000	0.020	-1	1	0.979	0.978	0.265	0.002	76.327	0.958	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.018	0.003	0.000	0.018	-1	1	1.000	1.000	0.005	0.000	3.341	0.539	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.264	0.000	1	5	NaN	NaN	0.098	0.000	0.619	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.426	0.082	0.000	0.019	1	5	0.988	0.982	0.311	0.002	62.429	0.417	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.000	0.000	0.023	1	5	1.000	1.000	0.005	0.000	4.303	0.275	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.310	0.000	-1	100	NaN	NaN	0.097	0.000	0.533	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.013	0.192	0.000	0.030	-1	100	0.990	0.982	0.310	0.004	96.773	1.457	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.001	0.000	0.028	-1	100	1.000	1.000	0.005	0.000	5.317	0.411	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.305	0.000	-1	5	NaN	NaN	0.096	0.000	0.545	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.435	0.000	0.000	0.030	-1	5	0.988	0.978	0.266	0.004	114.329	1.629	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.001	0.000	0.028	-1	5	1.000	1.000	0.006	0.000	5.071	0.344	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.017	0.000	0.027	0.000	-1	5	NaN	NaN	0.668	0.000	4.517	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.791	0.010	0.000	0.001	-1	5	0.973	0.984	0.524	0.025	1.508	0.074	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.001	0.000	5.356	2.140	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.957	0.000	0.027	0.000	-1	100	NaN	NaN	0.638	0.000	4.635	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.550	0.019	0.000	0.003	-1	100	0.973	0.982	0.168	0.003	15.216	0.275	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	100	1.000	1.000	0.000	0.000	15.412	8.143	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.824	0.000	0.028	0.000	-1	1	NaN	NaN	0.649	0.000	4.351	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.404	0.003	0.000	0.000	-1	1	0.959	0.984	0.501	0.006	0.806	0.012	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	5.050	2.186	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.795	0.000	0.029	0.000	1	100	NaN	NaN	0.634	0.000	4.408	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.368	0.092	0.000	0.004	1	100	0.973	0.982	0.165	0.003	26.451	0.753	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	100	1.000	1.000	0.000	0.000	8.543	4.339	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.783	0.000	0.029	0.000	1	1	NaN	NaN	0.645	0.000	4.312	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.659	0.004	0.000	0.001	1	1	0.959	0.962	0.093	0.004	7.076	0.316	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	6.888	4.526	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.824	0.000	0.028	0.000	1	5	NaN	NaN	0.633	0.000	4.460	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.322	0.009	0.000	0.001	1	5	0.973	0.962	0.091	0.001	14.559	0.201	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	7.879	5.028	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.772	0.000	0.021	0.000	-1	5	NaN	NaN	0.455	0.000	1.699	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.028	0.001	0.001	0.000	-1	5	0.974	0.979	0.007	0.001	4.189	0.435	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	18.416	14.273	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.790	0.000	0.020	0.000	-1	100	NaN	NaN	0.465	0.000	1.700	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.000	0.000	0.000	-1	100	0.978	0.976	0.001	0.000	43.760	11.479	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.034	14.889	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.764	0.000	0.021	0.000	-1	1	NaN	NaN	0.432	0.000	1.771	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	1	0.969	0.979	0.006	0.001	4.173	0.461	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	23.187	18.169	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.828	0.000	0.019	0.000	1	100	NaN	NaN	0.432	0.000	1.915	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.054	0.002	0.000	0.000	1	100	0.978	0.976	0.001	0.000	52.715	15.396	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	6.142	4.667	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.754	0.000	0.021	0.000	1	1	NaN	NaN	0.433	0.000	1.742	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.000	0.001	0.000	1	1	0.969	0.971	0.001	0.000	33.026	12.276	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.839	4.794	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.749	0.000	0.021	0.000	1	5	NaN	NaN	0.433	0.000	1.727	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	1	5	0.974	0.971	0.001	0.000	37.253	13.648	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.864	4.709	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.592	0.000	0.811	0.000	k-means++	NaN	30	NaN	0.421	0.0	1.406	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.383	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.488	4.987	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.317	7.060	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.473	0.000	1.016	0.000	random	NaN	30	NaN	0.399	0.0	1.184	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.385	0.000	random	0.001	30	0.001	0.000	0.0	7.989	4.482	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.022	6.729	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.377	0.000	3.763	0.000	k-means++	NaN	30	NaN	2.805	0.0	2.274	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	15.338	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.626	2.312	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.019	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.668	6.687	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.672	0.000	4.231	0.000	random	NaN	30	NaN	2.653	0.0	2.138	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	13.094	0.000	random	0.003	30	0.002	0.000	0.0	6.689	3.541	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.419	5.925	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.238	0.000	0.013	0.000	k-means++	NaN	20	NaN	0.031	0.0	7.567	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.001	0.162	0.000	k-means++	-0.001	20	-0.000	0.001	0.0	3.076	1.043	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.382	6.048	See	See
3	KMeans_short	fit	10000	10000	2	20	0.078	0.000	0.041	0.000	random	NaN	20	NaN	0.086	0.0	0.910	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.190	0.000	random	-0.001	20	0.001	0.001	0.0	2.697	0.509	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.341	5.865	See	See
6	KMeans_short	fit	10000	10000	100	20	0.625	0.000	0.256	0.000	k-means++	NaN	20	NaN	0.135	0.0	4.627	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.000	6.554	0.000	k-means++	0.336	20	0.289	0.001	0.0	2.076	0.324	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.186	4.795	See	See
9	KMeans_short	fit	10000	10000	100	20	0.209	0.000	0.764	0.000	random	NaN	20	NaN	0.337	0.0	0.621	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.000	6.762	0.000	random	0.325	20	0.243	0.001	0.0	2.152	0.424	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.482	4.113	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.607	0.0	[-0.11123046]	0.000	NaN	NaN	NaN	NaN	NaN	1.769	0.0	5.995	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[54.32680767]	0.000	NaN	NaN	NaN	NaN	0.524	0.000	0.0	0.884	0.400	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.26096544]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.360	0.339	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.719	0.0	[2.89328651]	0.001	NaN	NaN	NaN	NaN	NaN	0.739	0.0	0.973	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.001	0.0	[139.24968653]	0.000	NaN	NaN	NaN	NaN	0.280	0.003	0.0	0.534	0.100	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[26.10736653]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.115	0.086	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.170	0.0	0.471	0.0	NaN	NaN	NaN	0.185	0.0	0.918	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	8.326	0.0	NaN	NaN	0.093	0.016	0.0	0.590	0.008	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.215	0.0	NaN	NaN	NaN	0.000	0.0	0.582	0.542	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.263	0.0	0.633	0.0	NaN	NaN	NaN	0.227	0.0	5.566	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	2.384	0.0	NaN	NaN	1.000	0.000	0.0	1.475	1.505	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.0	0.701	0.735	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()

Raw results

Randomized HPO Curves

25th percentile

Mean

75th percentile